code: def get_sym_conv(data, sym):
      all_layers = sym.get_internals()
  
      isize = 640
      _, out_shape, _ = all_layers.infer_shape(
          data=(1, 3, isize, isize)
      )
      last_entry = None
      c1 = None
      c2 = None
      c3 = None
      c1_name = None
      c2_name = None
      c3_name = None
      c1_filter = -1
      c2_filter = -1
      c3_filter = -1
      # print(len(all_layers), len(out_shape))
      # print(all_layers.__class__)
      outputs = all_layers.list_outputs()
      # print(outputs.__class__, len(outputs))
      count = len(outputs)
      stride2name = {}
      stride2layer = {}
      stride2shape = {}
      for i in range(count):
          name = outputs[i]
          shape = out_shape[i]
          print(i, name, count, shape)
          if not name.endswith("_output"):
              continue
          if len(shape) != 4:
              continue
          assert isize % shape[2] == 0
          if shape[1] > config.max_feat_channel:
              break
          stride = isize // shape[2]
          stride2name[stride] = name
          stride2layer[stride] = all_layers[name]
          stride2shape[stride] = shape
  
      strides = sorted(stride2name.keys())
      for stride in strides:
          print(
              "stride",
              stride,
              stride2name[stride],
              stride2shape[stride],
          )
      print("F1_F2", F1, F2)
      # print('cnames', c1_name, c2_name, c3_name, F1, F2)
      _bwm = 1.0
      ret = {}
      sym_buffer = {}
      for stride in [4, 8, 16, 32]:
          sym_buffer["C%d" % stride] = stride2layer[stride]
      if not config.USE_FPN:
          for stride in config.RPN_FEAT_STRIDE:
              name = "L%d" % stride
              ret[stride] = get_sym_by_name(name, sym_buffer)
      else:
          for stride in config.RPN_FEAT_STRIDE:
              name = "P%d" % stride
              ret[stride] = get_sym_by_name(name, sym_buffer)
  
      return ret
  
file_path: insightface/detection/retinaface/rcnn/symbol/symbol_common.py
imports: import mxnet
  import mxnet.ndarray
  import numpy
  from rcnn.config import config
  from rcnn.PY_OP import rpn_fpn_ohem3, cascade_refine
label_desc: Use image node_id 11 for calling a global function with example usage: get_sym_conv(data, sym) and returns: ret